Method and apparatus for identifying structural deformation

ABSTRACT

A method and apparatus for identifying deformation of a structure. Training deformation data is identified for each training case in a plurality of training cases. Training strain data is identified for each training case in the plurality of training cases. The training deformation data and the training strain data are configured for use by a heuristic model to increase an accuracy of output data generated by the heuristic model. A group of parameters for the heuristic model is adjusted using the training deformation data and the training strain data for the each training case in the plurality of training cases such that the heuristic model is trained to generate estimated deformation data for the structure based on input strain data. The estimated deformation data has a desired level of accuracy.

GOVERNMENT LICENSE RIGHTS

This application was made with United States Government support underUnited States Air Force AFRL FA8650-08-D-3857 TO 0011 (CCN 9WECY530)awarded by Department of Defense. The United States Government hascertain rights in this application.

BACKGROUND INFORMATION

1. Field

The present disclosure relates generally to structural deformation and,in particular, to identifying structural deformation. Still moreparticularly, the present disclosure relates to a method and apparatusfor identifying the deformation of a structure using measured straindata and a heuristic model.

2. Background

Some structures associated with a platform experience deformation duringoperation of the platform. As used herein, the “deformation” of astructure is any change in the shape of the structure from a referenceshape for the structure. Typically, a structure associated with aplatform deforms in response to one or more loads being applied to thestructure during operation of the platform. Deformation of the structureduring operation of the platform may reduce a performance of thestructure from a desired level of performance.

As one illustrative example, an antenna system associated with anaircraft may deform in response to a number of loads and/or pressureapplied to the antenna system during flight of the aircraft. Deformationof the antenna system reduces performance of the antenna system. Inparticular, deformation of the antenna system may cause the antennasystem to operate outside of selected tolerances.

In one illustrative example, the antenna system may be a phased arrayantenna system. Deformation of this type of antenna system may affectthe electronic beam steering capabilities of the antenna system morethan desired. For example, the beam formed by the antenna system may besteered in a direction outside of selected tolerances with respect to adesired direction for the beam. This type of steering may occur when atleast a portion of the antenna system deforms. Identifying the amount ofdeformation experienced by the antenna system can be used toelectronically compensate for this deformation.

Some currently available systems for identifying the deformation of astructure associated with a platform include using optical systems,imaging systems, fiber optic systems, coordinate measuring machine (CMM)systems, cameras, and/or other types of devices. These different devicesare used to identify the deformation of a structure associated with aplatform during operation of the platform.

However, these currently available systems may be unable to identify thedeformation of the structure with a desired level of accuracy. Further,these currently available systems for identifying the deformation of astructure may be more complex, time-consuming, and/or expensive thandesired. Therefore, it would be desirable to have a method and apparatusthat takes into account one or more of the issues discussed above aswell as possibly other issues.

SUMMARY

In one illustrative embodiment, a method for identifying deformation ofa structure is provided. Training deformation data is identified foreach training case in a plurality of training cases. Training straindata is identified for each training case in the plurality of trainingcases. The training deformation data and the training strain data areconfigured for use by a heuristic model to increase an accuracy ofoutput data generated by the heuristic model. A group of parameters forthe heuristic model is adjusted using the training deformation data andthe training strain data for the each training case in the plurality oftraining cases such that the heuristic model is trained to generateestimated deformation data for the structure based on input strain data.The estimated deformation data has a desired level of accuracy.

In another illustrative embodiment, a method for managing performance ofa structure is provided. Training deformation data and training straindata are identified for the structure for each training case in aplurality of training cases. Each training case is configured for use bya heuristic model to increase an accuracy of output data generated bythe heuristic model. The structure is configured for association with aplatform. A group of parameters for the heuristic model is adjustedusing the training deformation data and the training strain data foreach training case in the plurality of training cases such that theheuristic model is trained to generate estimated deformation data forthe structure based on input strain data. The estimated deformation datahas a desired level of accuracy. Strain data for the structure isgenerated using a sensor system associated with the structure duringoperation of a platform when the structure is associated with theplatform. The estimated deformation data for the structure is generatedusing the heuristic model and the strain data as the input strain datafor the heuristic model. A group of control parameters for the structureis adjusted using the estimated deformation data generated by theheuristic model such that the structure has a desired level ofperformance during the operation of the platform.

In yet another illustrative embodiment, an apparatus comprises aheuristic model and a trainer. The heuristic model is configured togenerate estimated deformation data for a structure based on inputstrain data. The estimated deformation data has a desired level ofaccuracy. The trainer is configured to identify training deformationdata and training strain data for each training case in a plurality oftraining cases. The trainer is further configured to train the heuristicmodel using the training deformation data and the training strain dataidentified for each training case in the plurality of training casessuch that the heuristic model generates the estimated deformation datafor the structure with a desired level of accuracy based on the inputstrain data.

The features and functions can be achieved independently in variousembodiments of the present disclosure or may be combined in yet otherembodiments in which further details can be seen with reference to thefollowing description and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features believed characteristic of the illustrativeembodiments are set forth in the appended claims. The illustrativeembodiments, however, as well as a preferred mode of use, furtherobjectives and features thereof, will best be understood by reference tothe following detailed description of an illustrative embodiment of thepresent disclosure when read in conjunction with the accompanyingdrawings, wherein:

FIG. 1 is an illustration of a block diagram of a training environmentin accordance with an illustrative embodiment;

FIG. 2 is an illustration of a training environment in accordance withan illustrative embodiment;

FIG. 3 is an illustration of a plurality of sensors associated with aphased array antenna in accordance with an illustrative embodiment;

FIG. 4 is an illustration of a table of estimated deformation data inaccordance with an illustrative embodiment;

FIG. 5 is an illustration of a table of actual deformation data inaccordance with an illustrative embodiment;

FIG. 6 is an illustration of a table of differences between estimateddeformation measurements and actual deformation measurements inaccordance with an illustrative embodiment;

FIG. 7 is an illustration of a flowchart of a process for managing theperformance of a structure in the form of a flowchart in accordance withan illustrative embodiment;

FIG. 8 is an illustration of a flowchart of a process for training aheuristic model in the form of a flowchart in accordance with anillustrative embodiment;

FIG. 9 is an illustration of a flowchart of a process for training aheuristic model in the form of a flowchart in accordance with anillustrative embodiment;

FIG. 10 is an illustration of a flowchart of a process for identifying aconfiguration of sensors for use on a structure in the form of aflowchart in accordance with an illustrative embodiment;

FIG. 11 is an illustration of a comparison of graphs for the peaksidelobe ratio of a phased array antenna in accordance with anillustrative embodiment;

FIG. 12 is an illustration of a comparison of graphs for a reduction ingain in accordance with an illustrative embodiment;

FIG. 13 is an illustration of a comparison of graphs for phase inaccordance with an illustrative embodiment;

FIG. 14 is an illustration of a comparison of graphs for beam steeringangle deviation in accordance with an illustrative embodiment; and

FIG. 15 is an illustration of a data processing system in accordancewith an illustrative embodiment.

DETAILED DESCRIPTION

The different illustrative embodiments recognize and take into accountone or more different considerations. For example, the differentillustrative embodiments recognize and take into account that somecurrently available systems for measuring the deformation of a structuremay not provide a desired level of accuracy. For example, thesecurrently available systems may be unable to provide the level ofaccuracy needed to compensate for the deformation of the structure.

Optical systems comprising devices, such as, for example,three-dimensional coordinate measuring machine systems, fiber opticsystems, cameras, and/or other suitable devices, may be unable tomeasure the three-dimensional deformation of a structure associated withan aircraft, while in flight, with a desired level of accuracy. Thedifferent illustrative embodiments recognize and take into account thatthese devices may not provide the desired level of spatial resolutionneeded to measure the deformed shape of the structure with the desiredlevel of accuracy.

The different illustrative embodiments also recognize that opticalsystems having cameras require that these cameras be pointed at thestructure. Further, operating these optical systems in certainenvironmental conditions may be more difficult than desired. Forexample, operating these optical systems in conditions such as, rain,extreme temperatures, wind, snow, nighttime, low light levels, fog,and/or other conditions may be more difficult than desired.Additionally, measuring a three-dimensional shape of a structure usingan optical system having cameras may involve using multiple views. Usingmultiple views may increase the processing resources, effort, and/ortime needed to measure the three-dimensional shape of the structure.

The different illustrative embodiments recognize and take into accountthat a phased array antenna on an aircraft may be deformed during flightof the aircraft. The different illustrative embodiments also recognizeand take into account that it may be desirable to have a systemconfigured to identify the deformation of the phased array antenna withthe level of accuracy needed to electronically beam steer a phased arrayantenna to compensate for the deformation of the phased array antennaduring flight of the aircraft, within selected tolerances.

Further, the different illustrative embodiments recognize and take intoaccount that it may be desirable to have a system capable of identifyingthe deformation of the phased array antenna and electronically beamsteering the phased array antenna to compensate for this deformation insubstantially real-time. In this manner, undesired changes orinconsistencies in the performance of the phased array antenna caused bydeformation of the phased array antenna during flight of the aircraftmay be reduced and, in some cases, prevented.

Thus, the different illustrative embodiments provide a method andapparatus for managing the performance of a structure. In oneillustrative embodiment, a method for identifying deformation of astructure is provided. Training deformation data is identified for eachtraining case in a plurality of training cases. Training strain data isidentified for each training case in the plurality of training cases.The training deformation data and the training strain data areconfigured for use by a heuristic model to increase an accuracy ofoutput data generated by the heuristic model. A group of parameters forthe heuristic model is adjusted using the training deformation data andthe training strain data for the each training case in the plurality oftraining cases such that the heuristic model is trained to generateestimated deformation data for the structure based on input strain data.The estimated deformation data has a desired level of accuracy.

The estimated deformation data may be used to adjust a group of controlparameters for the structure such that the structure has a desired levelof performance during the operation of the platform. In particular, theestimated deformation data may be used to control the structure in amanner that compensates for the deformation of the structure during theoperation of the platform.

Referring now to the figures, and in particular, with reference to FIG.1, an illustration of a training environment is depicted in accordancewith an illustrative embodiment. In these illustrative examples,training environment 100 includes trainer 102. Trainer 102 is configuredto train heuristic model 104 to identify deformation 106 of structure108 associated with platform 110.

As used herein, when one component is “associated” with anothercomponent, this association is a physical association in these depictedexamples. For example, a first component, such as structure 108, may beconsidered to be associated with a second component, such as platform110, by being secured to the second component, bonded to the secondcomponent, mounted to the second component, welded to the secondcomponent, fastened to the second component, and/or connected to thesecond component in some other suitable manner. The first component alsomay be connected to the second component using a third component. Thefirst component may also be considered to be associated with the secondcomponent by being formed as part of and/or an extension of the secondcomponent.

In these illustrative examples, platform 110 may be, for example,without limitation, an aircraft, a helicopter, a jet, an unmanned aerialvehicle (UAV), a space shuttle, an automobile, a rocket, a missile, awatercraft, a propulsion system, a building, a manmade structure, abridge, a satellite, or some other suitable type of platform. Structure108 associated with platform 110 may be, for example, withoutlimitation, an imaging system, a communications system, an antennasystem, a phased array antenna system, a wing, a skin panel, a cable, arod, a beam, or some other suitable type of structure.

In one illustrative example, platform 110 is an aircraft, and structure108 is a phased array antenna system. In this illustrative example, thephased array antenna is associated with the aircraft by being integratedinto one or more other structures of the aircraft. For example, thephased array antenna may be integrated into a wing, a stabilizer, a skinpanel, or a door of the aircraft.

Deformation 106 of structure 108 is any change in shape 112 of structure108 from reference shape 114 of structure 108. In one illustrativeexample, reference shape 114 is the shape of structure 108 without anyloads or pressure being applied to structure 108.

When one or more loads and/or pressure is applied to structure 108,structure 108 may deform such that shape 112 of structure 108 changesfrom reference shape 114 to deformed shape 116. The loads and/orpressure applied to structure 108 may include, for example, withoutlimitation, aerodynamic loads, gusts, vibrations in structure 108,static loads, aero-acoustic loads, temperature-based loads, and/or othersuitable types of loads and/or pressures.

Structure 108 may deform during operation of platform 110. Whenstructure 108 has deformed shape 116, structure 108 may operate outsideof selected tolerances. An identification of deformed shape 116 forstructure 108 may be used to adjust group of control parameters 115 forstructure 108.

As used herein, a “group of” items means one or more items. For example,group of control parameters 115 means one or more control parameters115. Group of control parameters 115 may be adjusted to manage theperformance of structure 108 such that structure 108 operates andperforms within selected tolerances. In these illustrative examples,“adjusting” a group of parameters, such as group of control parameters115 may include changing one, some, all, or none of the parameters inthe group of parameters.

As one illustrative example, when platform 110 is an aircraft andstructure 108 is a phased array antenna system integrated into theaircraft, the phased array antenna system may deform into deformed shape116, while the aircraft is in flight. When the phased array antennasystem has deformed shape 116, the phased array antenna system mayoperate outside of selected tolerances.

An identification of deformed shape 116 may be used to adjust a phaseand/or amplitude of the phased array antenna system to electronicallysteer a beam formed by the phased array antenna system to compensate fordeformation 106, while platform 110 is in flight. When deformation 106is electronically compensated in this manner, the phased array antennasystem operates within selected tolerances during flight. In particular,this system operates within the selected tolerances during flight evenwhen the phased array antenna system has deformed shape 116.

Heuristic model 104 can be trained to identify deformation 106 ofstructure 108, thereby identifying deformed shape 116 of structure 108.In these illustrative examples, identifying deformation 106 of structure108 may comprise estimating deformation 106 of structure 108 with adesired level of accuracy. In this manner, deformed shape 116 ofstructure 108 may be estimated with a desired level of accuracy.

As used herein, a “heuristic model”, such as heuristic model 104, may beany mathematical or computational model configured to learn, adapt, makedecisions, find patterns in data, remember data, and/or processinformation in some other suitable manner to generate output data 118 inresponse to receiving input data 120.

Heuristic model 104 may comprise any number of learning algorithms,decision-making models, problem solving-models, computationalalgorithms, and/or other types of processes. In these illustrativeexamples, heuristic model 104 comprises at least one of a neuralnetwork, a learning-based algorithm, a regression model, a supportvector machine, a data fitting model, a pattern recognition model,artificial intelligence (AI), and some other suitable type of algorithmor model.

As used herein, the phrase “at least one of”, when used with a list ofitems, means different combinations of one or more of the listed itemsmay be used and only one of each item in the list may be needed. Forexample, “at least one of item A, item B, and item C” may include,without limitation, item A or item A and item B. This example also mayinclude item A, item B, and item C, or item B and item C. In otherexamples, “at least one of” may be, for example, without limitation, twoof item A, one of item B, and ten of item C; four of item B and seven ofitem C; and other suitable combinations.

As depicted in these examples, heuristic model 104 is configured togenerate output data 118 in response to receiving input data 120 basedon group of parameters 121. Group of parameters 121 may include, forexample, without limitation, biases, weights, coefficients,relationships, constants, constraints, and/or other suitable types ofparameters. In one illustrative example, heuristic model 104 may includean equation comprising biases and weights configured to produce outputdata 118 in response to receiving input data 120.

In these illustrative examples, trainer 102 is configured to trainheuristic model 104 to estimate deformation 106 of structure 108 with adesired level of accuracy. Estimating deformation 106 of structure 108with a desired level of accuracy means estimating deformation 106 suchthat a difference between the estimated deformation of structure 108 andthe actual deformation of structure 108 is within selected tolerances.

Trainer 102 may be implemented using hardware, software, or acombination of both in these examples. For example, trainer 102 may beimplemented in computer system 122. Computer system 122 comprises anumber of computers. As used herein, a “number of” items means one ormore items. For example, a number of computers means one or morecomputers.

When more than one computer is present in computer system 122, thesecomputers are in communication with each other. The different computersin computer system 122 may be located on platform 110, on structure 108,and/or remote to platform 110.

In one illustrative example, heuristic model 104 generates output data118 in the form of estimated deformation data 124 in response toreceiving input data 120 in the form of input strain data 126. Estimateddeformation data 124 defines the estimated deformed shape for structure108 based on input strain data 126.

As used herein, “deformation data”, such as estimated deformation data124, comprises a plurality of estimated deformation measurements. A“plurality of” items, as used herein, means two or more items. Forexample, a plurality of estimated deformation measurements means two ormore estimated deformation measurements.

In these depicted examples, a deformation measurement is a measurementof the deflection of a point on structure 108 from the location of thepoint when structure 108 has reference shape 114, to the location of thepoint when structure 108 has deformed shape 116. As used herein, the“deflection” of a point on structure 108 is the distance between thepoint when the structure 108 has reference shape 114 and the point whenthe structure 108 has deformed shape 116. This deflection of the pointmay be also referred to as a displacement of the point.

In these illustrative examples, the measurement of the deflection of thepoint may be in units of length. Units of length include, for example,without limitation, inches, feet, centimeters, millimeters, and othertypes of units of length. Of course, in other illustrative examples, themeasurement of the deflection of the point may be in angular units.Angular units include, for example, without limitation, radians,degrees, and other types of angular units.

Further, as used herein, “strain data”, such as input strain data 126,comprises a plurality of strain measurements. A strain measurement is ameasurement of the deflection of a point on structure 108 from thelocation of the point when structure 108 has reference shape 114 to thelocation of the point when structure 108 has deformed shape 116,normalized relative to a reference length. A strain measurement does nothave any units and may be represented as a percentage, a fraction, or aparts-per-notation (ppn).

Heuristic model 104 may receive input strain data 126 in a number ofdifferent ways. As one illustrative example, input strain data 126 maybe received as strain data 128 generated by sensor system 130. Sensorsystem 130 is associated with structure 108. In some illustrativeexamples, a portion of sensor system 130 may be associated with platform110.

Sensor system 130 comprises plurality of sensors 132 configured togenerate strain data 128. Strain data 128 comprises a plurality ofstrain measurements generated by plurality of sensors 132, respectively.A sensor in plurality of sensors 132 may comprise at least one of, forexample, a strain gauge, a fiber-optic sensor, a piezoelectric sensor, atransducer, or some other suitable type of sensor configured to generatestrain measurements.

In some illustrative examples, input data 120 may also includeadditional input data 133 in addition to input strain data 126.Additional input data 133 may include any data that may affect outputdata 118 generated by heuristic model 104 based on input strain data126. In particular, additional input data 133 may include any data aboutconditions that may affect deformation 106 of structure 108 whileplatform 110 operates.

For example, additional input data 133 may include environmental datasuch as, for example, measurements of environmental conditions that mayaffect deformation 106 of structure 108 and/or strain data 128. Thisenvironmental data may include, for example, temperature data, humiditydata, and/or other suitable types of data. In some cases, additionalinput data 133 may include data from an inertial measurement unit (IMU)attached to platform 110, position data, altitude data, velocity data,acceleration data, and/or other suitable types of data.

In these illustrative examples, trainer 102 trains heuristic model 104using plurality of training cases 136 selected for training heuristicmodel 104. As used herein, a “training case”, such as a training case inplurality of training cases 136 is a particular state for structure 108in which data about structure 108, when structure 108 is in thisparticular state, is used to train heuristic model 104. The particularstate for structure 108 may be, for example, a particular deformed shapefor structure 108. However, in some cases, the particular state forstructure 108 may be a selected amount of loading and/or pressure beingapplied to structure 108.

Trainer 102 identifies training deformation data and training straindata for each training case in plurality of training cases 136. Trainingcase 140 is an example of one of plurality of training cases 136.Further, trainer 102 identifies training deformation data 142 andtraining strain data 144 for training case 140.

Trainer 102 sends training deformation data 142 and training strain data144 to heuristic model 104. Heuristic model 104 uses trainingdeformation data 142 and training strain data 144 to adjust group ofparameters 121 for heuristic model 104. Group of parameters 121 areadjusted such that heuristic model 104 is capable of generatingestimated deformation data 124 for structure 108 with a desired level ofaccuracy based on input strain data 126. In these illustrative examples,trainer 102 trains heuristic model 104 using plurality of training cases136 and an iterative process.

The training deformation data and training strain data identified foreach training case in plurality of training cases 136 may be identifiedin a number of different ways in training environment 100. For example,training environment 100 may be a laboratory, a testing facility, a windtunnel, or some other type of training environment in which the trainingdeformation data and the training strain data can be generated. In somecases, training environment 100 may be the actual environment in whichplatform 110 operates. In this manner, the training deformation data andthe training strain data may be gathered and collected in any number ofdifferent ways.

In one illustrative example, plurality of actuators 146 are used todeform structure 108 according to plurality of training cases 136. Forexample, plurality of actuators 146 may be used to cause structure 108to deform in a manner corresponding to training case 140. Morespecifically, plurality of actuators 146 may be used to apply aplurality of selected loads to plurality of points 148 on structure 108to cause plurality of points 148 to deflect in a manner that causesstructure 108 to have the deformed shape corresponding to training case140.

Of course, in other illustrative examples, some other type of system maybe used to cause structure 108 to deform in a manner corresponding toplurality of training cases 136. Depending on the implementation,structure 108 may be deformed for the purposes of training withstructure 108 separated from platform 110. In some illustrativeexamples, platform 110 may be operated with structure 108 associatedwith platform 110 to cause structure 108 to deform according toplurality of training cases 136.

When structure 108 has been deformed according to a particular trainingcase, training deformation data and training strain data for thattraining case are identified. The training deformation data and thetraining strain data may be referred to as a training data set, in someillustrative examples. The training deformation data may be identifiedin a number of different ways. As one illustrative example, trainer 102may identify the training deformation data using imaging data 150received from imaging system 152.

Imaging system 152 comprises any number of components configured togenerate imaging data 150 from which a plurality of deformationmeasurements can be identified. For example, imaging system 152 maycomprise an optical imaging system, a laser imaging system, an infraredimaging system, or some other suitable type of imaging system. In someillustrative examples, imaging data 150 may include a plurality ofdeformation measurements for use as the training deformation data.

Further, strain data 128 generated by sensor system 130 when structure108 is deformed according to a particular training case may be used asthe training strain data for that training case. Of course, in otherillustrative examples, sensor system 130 may generate other sensor datain addition to and/or in place of strain data 128. Trainer 102 may usethis other sensor data to identify the training strain data.

In these illustrative examples, the number of training cases inplurality of training cases 136 may be selected by the operator. Newtraining cases may be added to plurality of training cases 136 at anypoint in time such that heuristic model 104 can adapt to this new data.

Further, in some cases, trainer 102 may identify training environmentaldata for a training case, such as training case 140, to train heuristicmodel 104. This training environmental data may be identified in anumber of different ways. For example, historical environmental dataand/or test environmental data may be used. This training environmentaldata may be used to train heuristic model 104 such that estimateddeformation data 124 may be generated with the desired level of accuracybased on input strain data 126 when structure 108 is operated indifferent types of environmental conditions.

Once heuristic model 104 has been trained within training environment100, heuristic model 104 can be used in structure 108 to manage theperformance of structure 108 during operation of platform 110. Forexample, when structure 108 is a phased array antenna, heuristic model104 may be used in a processor unit associated with the phased arrayantenna. Estimated deformation data 124 generated by heuristic model 104during operation of platform 110 may be used to adjust group of controlparameters 115 for structure 108.

In these illustrative examples, group of control parameters 115 areadjusted to increase the performance of structure 108 to a desired levelof performance when structure 108 has deformed shape 116. Theperformance of structure 108 may be evaluated using group of performanceparameters 154 for structure 108. When structure 108 is a phased arrayantenna, group of performance parameters 154 may include, for example,without limitation, peak sidelobe ratio (PSLR), gain loss, beam steeringangle deviation, and/or other types of suitable performance parameters.

Estimated deformation data 124 may be used to calculate values foradjusting group of control parameters 115. Group of control parameters115 may be adjusted based on these values until group of performanceparameters 154 indicates that structure 108 has the desired level ofperformance when structure 108 has deformed shape 116. In this manner,estimated deformation data 124 is used to adjust group of controlparameters 115 to compensate for deformation 106 of structure 108 suchthat structure 108 maintains a desired level of performance.

In these illustrative examples, estimated deformation data 124 may beidentified for structure 108 and used to adjust group of controlparameters 115 for structure 108 during operation of platform 110 insubstantially real-time. These processes being performed in“substantially real-time” means that these processes are performedwithout any unintentional delays. In some cases, in “substantiallyreal-time” may mean immediately.

For example, without limitation, in response to structure 108 deformingfrom reference shape 114, heuristic model 104 is used to generateestimated deformation 124 for this deformation immediately. Estimateddeformation data 124 may then be used to immediately control structure108 by adjusting group of control parameters 115 to compensate for thisdeformation. In this manner, any change in a level of performance ofstructure 108 in response to the deformation of structure 108 may bereduced, and in some cases, prevented.

In this manner, the different illustrative embodiments provide a methodand apparatus for identifying deformation 106 of structure 108 andmanaging the performance of structure 108 based on this estimation.Further, the different illustrative embodiments provide a method andapparatus for training heuristic model 104 to generate estimateddeformation data 124 for structure 108 with a desired level of accuracyin response to receiving input data 120.

The illustration of training environment 100 in FIG. 1 is not meant toimply physical or architectural limitations to the manner in which anillustrative embodiment may be implemented. Other components in additionto or in place of the ones illustrated may be used. Some components maybe optional. Also, the blocks are presented to illustrate somefunctional components. One or more of these blocks may be combined,divided, or combined and divided into different blocks when implementedin an illustrative embodiment.

For example, in some cases, heuristic model 104 may comprise a pluralityof neural networks. Each neural network may be configured to generateestimated deformation data 124 for a particular point on structure 108.In these cases, each neural network may be trained to generate anestimated deformation measurement for the particular point on structure108 based on one or more input strain measurements at or near theparticular point on structure 108.

Further, each training case for each neural network may comprise one ormore training strain measurements and one training deformationmeasurement at the particular point. The training deformationmeasurement may be identified using a sensor at the particular point onstructure 108.

With reference now to FIG. 2, an illustration of a training environmentis depicted in accordance with an illustrative embodiment. In thisillustrative embodiment, training environment 200 is an example of oneimplementation for training environment 100 in FIG. 1. As depicted,computer system 202, support system 204, actuator system 206, imagingsystem 208, and sensor system 210 are present in training environment200.

Computer system 202 may be an example of one implementation for computersystem 122 in FIG. 1. A heuristic model, such as heuristic model 104 inFIG. 1, may be trained using computer system 202. In particular, theheuristic model may be trained using a trainer, such as, for example,trainer 102 in FIG. 1, implements in computer system 202.

As depicted, support system 204 is configured to hold and supportstructure 211. In this illustrative example, structure 211 is phasedarray antenna 212. Support system 204 supports and holds phased arrayantenna 212, while actuator system 206 applies a plurality of loads tophased array antenna 212 for selected training cases. As depicted,actuator system 206 comprises plurality of actuators 214 positionedrelative to a plurality of points on phased array antenna 212. Pluralityof actuators 214 is configured to apply a plurality of selected loads tothe plurality of points on phased array antenna 212 to cause phasedarray antenna 212 to deform in a manner corresponding to a particulartraining case. In these illustrative examples, applying a selected loadto a point on phased array antenna 212 causes that point to be deflectedfrom a reference position of that point by a selected amount.

Imaging system 208 is used to generate imaging data of phased arrayantenna 212. In this illustrative example, imaging system 208 comprisesplurality of cameras 216. The imaging data generated by plurality ofcameras 216 may be used to identify a plurality of deformationmeasurements at the plurality of points on phased array antenna 212. Inthis illustrative example, each deformation measurement may be adeflection of a corresponding point on phased array antenna 212 in adirection substantially perpendicular to phased array antenna 212.

For example, a trainer, such as trainer 102 in FIG. 1, may identifydeformation measurements for phased array antenna 212 using the imagingdata generated by plurality of cameras 216. These deformationmeasurements provide an indication of the deformed shape of phased arrayantenna 212.

Further, sensor system 210 generates a plurality of strain measurementsfor phased array antenna 212. Sensor system 210 comprises a plurality ofstrain gauges (not shown in this view) embedded into phased arrayantenna 212. The plurality of strain measurements generated by sensorsystem 210 and the plurality of deformation measurements identifiedusing imaging system 208 are sent to computer system 202 for processing.A trainer, such as trainer 102 in FIG. 1, uses these differentdeformation measurements and strain measurements to train a heuristicmodel to estimate the deformation of phased array antenna 212 with adesired level of accuracy based on strain data input into the heuristicmodel. The trainer may also use other information such as, for example,environmental data, to train the heuristic model.

The illustration of training environment 200 in FIG. 2 is not meant toimply physical or architectural limitations to the manner in which anillustrative embodiment may be implemented. Other components in additionto or in place of the ones illustrated may be used. Some components maybe optional.

Further, the different components shown in FIG. 2 may be combined withcomponents in FIG. 1, used with components in FIG. 1, or a combinationof the two. Additionally, some of the components in FIG. 2 may beillustrative examples of how components shown in block form in FIG. 1can be implemented as physical structures.

With reference now to FIG. 3, an illustration of a plurality of sensorsassociated with a phased array antenna is depicted in accordance with anillustrative embodiment. In this illustrative example, phased arrayantenna 300 is an example of one implementation for phased array antenna212 in FIG. 2. Further, phased array antenna 300 is an example of oneimplementation for structure 108 in FIG. 1.

Phased array antenna 300 has an array of antenna elements located withinportion 302 of phased array antenna 300. Plurality of sensors 304 arepositioned at plurality of points 306 on phased array antenna 300 inthis depicted example. As depicted, a portion of plurality of sensors304 are located within portion 302 of phased array antenna 300 andanother portion of plurality of sensors 304 are located outside ofportion 302 of phased array antenna 300.

Plurality of sensors 304 may take the form of, for example, withoutlimitation, a plurality of strain gauges. Each strain gauge isconfigured to generate a strain measurement at the point on phased arrayantenna 300 at which the strain gauge is located. For example, sensor308 at point 310 within portion 302 of phased array antenna 300 isconfigured to generate a strain measurement at point 310.

In this manner, plurality of sensors 304 generates a plurality of strainmeasurements that form strain data for use as input strain data for aheuristic model, such as heuristic model 104 in FIG. 1. In particular,the strain measurements generated by plurality of sensors 304 are anexample of strain data 128 in FIG. 1 that may be used as input straindata 126 for heuristic model 104 in FIG. 1. Further, the strainmeasurements generated by plurality of sensors 304 may be used to trainheuristic model 104 in FIG. 1.

With reference now to FIG. 4, an illustration of a table of estimateddeformation data is depicted in accordance with an illustrativeembodiment. In this illustrative example, table 400 includes pointidentifiers 402, training case 404, training case 406, training case408, training case 410, and training case 412.

Point identifiers 402 identify the points on a structure for whichestimated deformation measurements 413 are generated by a heuristicmodel, such as heuristic model 104 in FIG. 1, that has been trained by,for example, trainer 102 in FIG. 1. In this illustrative example, thepoints identified by point identifiers 402 are a selected combination ofpoints from plurality of points 306 on phased array antenna 300 in FIG.3.

Training case 404, training case 406, training case 408, training case410, and training case 412 each correspond to a particular deformedshape for phased array antenna 300. For each of these training cases,the sensors in plurality of sensors 304 in FIG. 3 positioned at thepoints identified in point identifiers 402 generate strain measurementsfor these points when phased array antenna 300 is deformed into adeformed shape corresponding to the training case. These strainmeasurements are input into the heuristic model to generate estimateddeformation measurements 413 for these same points.

With reference now to FIG. 5, an illustration of a table of actualdeformation data is depicted in accordance with an illustrativeembodiment. In this illustrative example, table 500 includes pointidentifiers 502, training case 504, training case 506, training case508, training case 510, and training case 512.

Point identifiers 502 identify the points on a structure for whichactual deformation measurements 513 are identified. Actual deformationmeasurements 513 are the deformation measurements identified for thepoints on the structure when the structure has actually been deformed.Actual deformation measurements 513 for these training cases may beidentified using, for example, imaging data 150 generated by imagingsystem 152 in FIG. 1.

In this illustrative example, the points identified in point identifiers502 are a selected combination of points from plurality of points 306 onphased array antenna 300 in FIG. 3. In particular, the points identifiedby point identifiers 502 are the same points identified by pointidentifiers 402 in FIG. 4.

Training case 504, training case 506, training case 508, training case510, and training case 512 are the same as training case 404, trainingcase 406, training case 408, training case 410, and training case 412,respectively.

Turning now to FIG. 6, an illustration of a table of differences betweenestimated deformation measurements and actual deformation measurementsis depicted in accordance with an illustrative embodiment. In thisillustrative example, table 600 includes point identifiers 602. In thisillustrative example, point identifiers 602 identifies the same pointsidentified by point identifiers 502 in FIG. 5 and point identifiers 402in FIG. 4.

Table 600 presents difference values 604 for training case 606, trainingcase 608, training case 610, training case 612, and training case 614.Training case 606, training case 608, training case 610, training case612, and training case 614 are the same as training case 404, trainingcase 406, training case 408, training case 410, and training case 412,respectively, in FIG. 4. Further, training case 606, training case 608,training case 610, training case 612, and training case 614 are the sameas training case 504, training case 506, training case 508, trainingcase 510, and training case 512, respectively, in FIG. 5.

Difference values 604 are the differences between estimated deformationmeasurements 413 in FIG. 4 and actual deformation measurements 513 inFIG. 5. In this illustrative example, difference values 604 indicatethat estimated deformation measurements 413 have the desired level ofaccuracy.

With reference now to FIG. 7, an illustration of a process for managingthe performance of a structure in the form of a flowchart is depicted inaccordance with an illustrative embodiment. The process illustrated inFIG. 7 may be implemented using trainer 102, heuristic model 104, andstructure 108 in FIG. 1.

The process begins by training a heuristic model to generate estimateddeformation data for a structure with a desired level of accuracy basedon input strain data for the structure (operation 700). The structuremay be, for example, structure 108 in FIG. 1. The structure may beconfigured for association with a platform, such as platform 110 inFIG. 1. The structure may or may not be associated with the platformwhen the training data needed to train the heuristic model is collected.

Thereafter, the process identifies strain data generated by a sensorsystem associated with the structure during operation of the platform(operation 702). In operation 702, the structure is associated with theplatform and may experience loading and/or pressure applied to thestructure during operation of the platform. Further, in thisillustrative example, the sensor system comprises a plurality of straingauges attached to and/or embedded within the structure.

The process then generates estimated deformation data for the structureduring operation of the platform based on the strain data generated bythe sensor system (operation 704). The strain data generated by thesensor system forms the input strain data for the heuristic model.

The process then adjusts a group of control parameters for the structureusing the estimated deformation data for the structure to increase aperformance of the structure to a desired level of performance(operation 706), with the process terminating thereafter. As oneillustrative example, when the structure is a phased array antenna, theestimated deformation data is used to adjust a phase and/or an amplitudefor electronically steering a beam formed by the phased array antenna.For example, the estimated deformation data may be input into acompensation algorithm for the phased array antenna.

With reference now to FIG. 8, an illustration of a process for traininga heuristic model in the form of a flowchart is depicted in accordancewith an illustrative embodiment. The process illustrated in FIG. 8 maybe used to implement operation 700 in FIG. 7. Further, this process maybe implemented using trainer 102 in FIG. 1.

The process begins by identifying training deformation data for eachtraining case in a plurality of training cases (operation 800). Eachtraining case in the plurality of training cases corresponds to at leastone of a particular deformed shape for a structure, such as structure108 in FIG. 1, and a selected amount of loading and/or pressure to beapplied to the structure. In operation 800, the training deformationdata may be identified using, for example, imaging data 150 generated byimaging system 152 in FIG. 1.

The process then identifies training strain data for each training casein the plurality of training cases (operation 802). In operation 802,the training strain data may be identified using, for example, straindata generated by a sensor system associated with the structure, suchas, for example, strain data 128 generated by sensor system 130associated with structure 108 in FIG. 1.

Thereafter, the process adjusts a group of parameters for the heuristicmodel using the training deformation data and the training strain datafor each case in the plurality of training cases such that the heuristicmodel is trained to generate estimated deformation data for thestructure with a desired level of accuracy based on input strain datafor the structure (operation 804), with the process terminatingthereafter. In particular, operation 804 may be performed such that theheuristic model generates estimated deformation data for the structurewith the desired level of accuracy during the operation of the platformwith which the structure is associated.

With reference now to FIG. 9, an illustration of a process for traininga heuristic model in the form of a flowchart is depicted in accordancewith an illustrative embodiment. The process illustrated in FIG. 9 maybe used to implement operation 700 in FIG. 7. Further, this process maybe a more detailed process of the process described in FIG. 8.

The process begins by selecting a training case from a plurality oftraining cases (operation 900). In this illustrative example, eachtraining case in the plurality of training cases specifies a particulardeformed shape for the structure. The process then deforms the structuresuch that the structure has the deformed shape specified by the selectedtraining case (operation 902).

Thereafter, the process identifies training deformation data for thestructure having the deformed shape specified by the selected trainingcase (operation 904). Operation 904 may be performed using, for example,an imaging system. The training deformation data identified in operation904 comprises a plurality of deformation measurements identified for aplurality of points on the structure.

The process also identifies training strain data for the structurehaving the deformed shape specified by the selected training case(operation 906). Operation 906 may be performed using a sensor systemassociated with the structure. The sensor system comprises a pluralityof sensors. Each sensor generates a strain measurement for a particularpoint on the structure at which the sensor is positioned. In thismanner, training strain data comprises a plurality of strainmeasurements for a plurality of points on the structure.

In this illustrative example, the plurality of deformation measurementsin the training deformation data and the plurality of strainmeasurements in the training strain data are generated for a sameplurality of points on the structure. In this manner, each strainmeasurement generated at a point on the structure corresponds to adeformation measurement generated at the same point on the structure.

Next, the process selects one combination of strain measurements fromthe training strain data (operation 908). As used herein, a “combinationof strain measurements” is a selection of one or more of the pluralityof strain measurements generated by the plurality of sensors in thesensor system. The combination of strain measurements does not includemore than one strain measurement from a particular sensor in the sensorsystem. In this manner, a selection of a combination of strainmeasurements corresponds to a selection of a combination of sensors inthe sensor system. The combination of strain measurements selected mayinclude one, some, or all of the strain measurements.

The process then uses the selected combination of strain measurementsand a corresponding combination of deformation measurements in thetraining deformation data to train the heuristic model (operation 910).In operation 910, the heuristic model uses the selected combination ofstrain measurements and the corresponding combination of deformationmeasurements to adjust a group of parameters for the heuristic model.The group of parameters adjusted determines the estimated deformationdata that is generated by the heuristic model based on certain inputstrain data.

Thereafter, the process inputs the selected combination of strainmeasurements into the heuristic model to generate estimated deformationdata (operation 912). The process determines whether the estimateddeformation data has a desired level of accuracy (operation 914). Inoperation 914, the determination may be made based on whether adifference between the estimated deformation data generated by theheuristic model and the actual deformation data indicated in thetraining deformation data is within selected tolerances.

If the estimated deformation data does not have the desired level ofaccuracy, the process adjusts the group of parameters for the heuristicmodel (916) and then returns to operation 912. With reference again tooperation 914, if the estimated deformation data has the desired levelof accuracy, the process stores the selected combination of strainmeasurements, the corresponding combination of deformation measurements,and the values for the group of parameters (operation 918).

The process then determines whether any additional unprocessedcombinations of strain measurements are present (operation 920). If anyadditional unprocessed combinations of strain measurements are present,the process returns to operation 908 as described above to select a newunprocessed combination of strain measurements.

Otherwise, the process determines whether any additional training casesare present in the plurality of training cases (operation 922). If anyadditional unprocessed training cases are present, the process returnsto operation 900 as described above. Otherwise, the process terminates.In this manner, the process described in FIG. 1 trains the heuristicmodel to generate estimated deformation data for the structure with adesired level of accuracy using the plurality of training cases.

With reference now to FIG. 10, an illustration of a process foridentifying a configuration of sensors for use on a structure in theform of a flowchart is depicted in accordance with an illustrativeembodiment. The process described in FIG. 10 may be implemented toselect a number of sensors from plurality of sensors 132 in sensorsystem 130 for structure 108 in FIG. 1 and a configuration for theseselected sensors.

The process begins by identifying the estimated deformation datagenerated by the heuristic model based on each selected combination ofstrain measurements for each training case in the plurality of trainingcases for the heuristic model (operation 1000). The process then usesthe estimated deformation data generated based on each selectedcombination of strain measurements for each training case in theplurality of training cases to adjust a group of control parameters forthe structure (operation 1002).

Thereafter, the process identifies the combination of strainmeasurements with the minimum number of strain measurements needed forthe heuristic model to generate estimated deformation data for thestructure with the level of accuracy needed to provide a desired levelof performance for the structure when the estimated deformation data isused to adjust the group of control parameters for the structure(operation 1004), with the process terminating thereafter. For example,a compensation algorithm may use the estimated deformation datagenerated by the heuristic model to adjust the group of controlparameters for the structure such that the structure has a desired levelof performance.

The estimated deformation data has the desired level of accuracy when adifference between a group of performance parameters for the structurebased on adjustments to the group of control parameters, identifiedusing the estimated deformation data, and the group of performanceparameters for the structure based on adjustments to the group ofcontrol parameters, identified using actual deformation data, are withinselected tolerances. In operation 1004, the process determines whichcombination of strain measurements, that leads to the heuristic modelgenerating estimated deformation data with the desired level ofaccuracy, has the minimum number of sensors.

The flowcharts and block diagrams in the different depicted embodimentsillustrate the architecture, functionality, and operation of somepossible implementations of apparatuses and methods according to anillustrative embodiment. In this regard, each block in the flowcharts orblock diagrams may represent a module, segment, function, and/or aportion of an operation or step. For example, one or more of the blocksmay be implemented as program code, in hardware, or as a combination ofthe two. When implemented in hardware, the hardware may, for example,take the form of integrated circuits that are manufactured or configuredto perform one or more operations in the flowcharts or block diagrams.

In some alternative implementations of an illustrative embodiment, thefunction or functions noted in the blocks may occur out of the ordernoted in the figures. For example, in some cases, two blocks shown insuccession may be executed substantially concurrently, or the blocks maysometimes be performed in the reverse order, depending upon thefunctionality involved. Also, other blocks may be added in addition tothe illustrated blocks in a flowchart or block diagram.

With reference now to FIGS. 11-14, illustrations of comparisons betweengraphs for control parameters are depicted in accordance with anillustrative embodiment. In FIGS. 11-14, each pair of graphs comparesadjustments made to a control parameter for a phased array antenna usingestimated deformation data and actual deformation data.

The estimated deformation data may be generated using, for example,heuristic model 104 in FIG. 1. The estimated deformation data isgenerated by the heuristic model using strain data generated by a sensorsystem associated with the phased array antenna. In FIGS. 11-14, thevalues of the control parameters are presented in the different graphswith respect to the number of sensors in the sensor system associatedwith the phased array antenna.

With reference now to FIG. 11, an illustration of a comparison of graphsfor the peak sidelobe ratio of a phased array antenna is depicted inaccordance with an illustrative embodiment. Graph 1100 has horizontalaxis 1104 and vertical axis 1106. Graph 1102 has horizontal axis 1108and vertical axis 1110.

Both horizontal axis 1104 and horizontal axis 1108 represent a number ofpoints on the phased array antenna for which estimated deformationmeasurements and actual deformation measurements are identified. Bothvertical axis 1106 and vertical axis 1110 represent the peak sideloberatio, in decibels, selected for the phased array antenna.

However, curve 1112 in graph 1100 identifies the peak sidelobe ratiowhen estimated deformation data generated by a trained heuristic modelis used to adjust the phase and/or amplitude for the phased arrayantenna. Curve 1114 in graph 1102 identifies the peak sidelobe ratiowhen actual deformation data is used to adjust the phase and/oramplitude for the phased array antenna.

As depicted, these curves indicate that the peak sidelobe ratio selectedfor the phased array antenna based on the estimated deformation data iswithin selected tolerances of the peak sidelobe ratio selected for thephased array antenna based on the actual deformation data. In otherwords, the peak sidelobe ratio for the phased array antenna, when thephased array antenna is electronically compensated using the estimateddeformation data, and the peak sidelobe ratio for the phased arrayantenna when the phased array antenna is electronically compensatedusing the actual deformation data may be substantially equal withinselected tolerances.

With reference now to FIG. 12, an illustration of a comparison of graphsfor a reduction in gain is depicted in accordance with an illustrativeembodiment. In this illustrative example, graph 1200 has horizontal axis1204 and vertical axis 1206. Graph 1202 has horizontal axis 1208 andvertical axis 1210.

Both horizontal axis 1204 and horizontal axis 1208 represent a number ofsensors in the sensor system associated with the phased array antenna.Both vertical axis 1206 and vertical axis 1210 represent the reductionin gain for the phased array antenna, in decibels.

However, curve 1212 in graph 1200 identifies the reduction in gain whenestimated deformation data generated by a trained heuristic model isused to adjust the phase and/or amplitude for the phased array antenna.Curve 1214 in graph 1202 identifies the reduction in gain when actualdeformation data is used to adjust the phase and/or amplitude for thephased array antenna.

As depicted, these curves indicate that the reduction in gain for thephased array antenna based on the estimated deformation data is withinselected tolerances of the reduction in gain for the phased arrayantenna based on the actual deformation data. In other words, thereduction in gain for the phased array antenna when the phased arrayantenna is electronically compensated using the estimated deformationdata and the reduction in gain for the phased array antenna when thephased array antenna is electronically compensated using the actualdeformation data may be substantially equal within selected tolerances.

With reference now to FIG. 13, an illustration of a comparison of graphsfor phase is depicted in accordance with an illustrative embodiment. Inthis illustrative example, graph 1300 has horizontal axis 1304 andvertical axis 1306. Graph 1302 has horizontal axis 1308 and verticalaxis 1310.

Both horizontal axis 1304 and horizontal axis 1308 represent a number ofsensors in the sensor system associated with the phased array antenna.Both vertical axis 1306 and vertical axis 1310 represent the phaseselected for the phased array antenna, in degrees.

However, curve 1312 in graph 1300 identifies the phase when estimateddeformation data generated by a trained heuristic model is used toadjust the phase for the phased array antenna. Curve 1314 in graph 1302identifies the phase when actual deformation data is used to adjust thephase for the phased array antenna. As depicted, these curves indicatethat the phase selected for the phased array antenna based on theestimated deformation data is within selected tolerances of the phaseselected for the phased array antenna based on the actual deformationdata.

With reference now to FIG. 14, an illustration of a comparison of graphsfor beam steering angle deviation is depicted in accordance with anillustrative embodiment. In this illustrative example, graph 1400 hashorizontal axis 1404 and vertical axis 1406. Graph 1402 has horizontalaxis 1408 and vertical axis 1410.

Both horizontal axis 1404 and horizontal axis 1408 represent a number ofsensors in the sensor system associated with the phased array antenna.Both vertical axis 1406 and vertical axis 1410 represent the beamsteering angle deviation for the phased array antenna, in degrees.

However, curve 1412 in graph 1400 identifies the beam steering angledeviation when estimated deformation data generated by a trainedheuristic model is used to adjust the phase and/or amplitude for thephased array antenna. Curve 1414 in graph 1402 identifies the beamsteering angle deviation when actual deformation data is used to adjustthe phase and/or amplitude for the phased array antenna.

As depicted, these curves indicate that the beam steering angledeviation for the phased array antenna based on the estimateddeformation data is within selected tolerances of the beam steeringangle deviation for the phased array antenna based on the actualdeformation data. In other words, the beam steering angle deviation forthe phased array antenna when the phased array antenna is electronicallycompensated using the estimated deformation data, and the beam steeringangle deviation for the phased array antenna when the phased arrayantenna is electronically compensated using the actual deformation datamay be substantially equal within selected tolerances.

The illustrations of graphs 1100 and 1102 in FIG. 11, graphs 1200 and1202 in FIG. 12, graphs 1300 and 1302 in FIG. 13, and graphs 1400 and1402 in FIG. 14 are not meant to imply physical or architecturallimitations to the manner in which an illustrative embodiment may beimplemented. The data presented in these graphs is with respect to onlyone possible implementation for a structure, a sensor system associatedwith that structure, and a heuristic model used to estimate deformationof the structure.

Turning now to FIG. 15, an illustration of a data processing system isdepicted in accordance with an illustrative embodiment. Data processingsystem 1500 may be used to implement computer system 122 in FIG. 1and/or computer system 202 in FIG. 2. In this illustrative example, dataprocessing system 1500 includes communications framework 1502, whichprovides communications between processor unit 1504, memory 1506,persistent storage 1508, communications unit 1510, input/output (I/O)unit 1512, and display 1514. In these examples, communications framework 1502 may be a bus system.

Processor unit 1504 serves to execute instructions for software that maybe loaded into memory 1506. Processor unit 1504 may be a number ofprocessors, a multi-processor core, or some other type of processor,depending on the particular implementation. Further, processor unit 1504may be implemented using a number of heterogeneous processor systems inwhich a main processor is present along with secondary processors on asingle chip. As another illustrative example, processor unit 1504 may bea symmetric multi-processor system containing multiple processors of thesame type.

Memory 1506 and persistent storage 1508 are examples of storage devices1516. A storage device is any piece of hardware that is capable ofstoring information, such as, for example, without limitation, data,program code in functional form, and/or other suitable types ofinformation either on a temporary basis and/or a permanent basis.Storage devices 1516 may also be referred to as computer readablestorage devices in these examples. Memory 1506, in these illustrativeexamples, may be, for example, a random access memory or any othersuitable volatile or non-volatile storage device. Persistent storage1508 may take various forms, depending on the particular implementation.

For example, persistent storage 1508 may contain one or more componentsor devices. For example, persistent storage 1508 may be a hard drive, aflash memory, a rewritable optical disk, a rewritable magnetic tape, orsome combination of the above. The media used by persistent storage 1508also may be removable. For example, a removable hard drive may be usedfor persistent storage 1508.

Communications unit 1510, in these examples, provides for communicationswith other data processing systems or devices. In these examples,communications unit 1510 is a network interface card. Communicationsunit 1510 may provide communications through the use of physical and/orwireless communications links.

Input/output unit 1512 allows for input and output of data with otherdevices that may be connected to data processing system 1500. Forexample, input/output unit 1512 may provide a connection for user inputthrough a keyboard, a mouse, and/or some other suitable input device.Further, input/output unit 1512 may send output to a printer. Display1514 provides a mechanism to display information to a user.

Instructions for the operating system, applications, and/or programs maybe located in storage devices 1516, which are in communication withprocessor unit 1504 through communications framework 1502. In theseillustrative examples, the instructions are in a functional form onpersistent storage 1508. These instructions may be loaded into memory1506 for execution by processor unit 1504. The processes of thedifferent embodiments may be performed by processor unit 1504 usingcomputer implemented instructions, which may be located in a memory,such as memory 1506.

These instructions are referred to as program code, computer usableprogram code, or computer readable program code that may be read andexecuted by a processor in processor unit 1504. The program code in thedifferent embodiments may be embodied on different physical or computerreadable storage media, such as memory 1506 or persistent storage 1508.

Program code 1518 is located in a functional form on computer readablemedia 1520 that is selectively removable and may be loaded onto ortransferred to data processing system 1500 for execution by processorunit 1504. Program code 1518 and computer readable media 1520 formcomputer program product 1522 in these illustrative examples. In oneexample, computer readable media 1520 may be computer readable storagemedia 1524 or computer readable signal media 1526. Computer readablestorage media 1524 may include, for example, an optical or magnetic diskthat is inserted or placed into a drive or other device that is part ofpersistent storage 1508 for transfer onto a storage device, such as ahard drive, that is part of persistent storage 1508. Computer readablestorage media 1524 also may take the form of a persistent storage, suchas a hard drive, a thumb drive, or a flash memory, that is connected todata processing system 1500. In some instances, computer readablestorage media 1524 may not be removable from data processing system1500. In these examples, computer readable storage media 1524 is aphysical or tangible storage device used to store program code 1518rather than a medium that propagates or transmits program code 1518.Computer readable storage media 1524 is also referred to as a computerreadable tangible storage device or a computer readable physical storagedevice. In other words, computer readable storage media 1524 is a mediathat can be touched by a person.

Alternatively, program code 1518 may be transferred to data processingsystem 1500 using computer readable signal media 1526. Computer readablesignal media 1526 may be, for example, a propagated data signalcontaining program code 1518. For example, computer readable signalmedia 1526 may be an electromagnetic signal, an optical signal, and/orany other suitable type of signal. These signals may be transmitted overcommunications links, such as wireless communications links, opticalfiber cable, coaxial cable, a wire, and/or any other suitable type ofcommunications link. In other words, the communications link and/or theconnection may be physical or wireless in these illustrative examples.

In some illustrative embodiments, program code 1518 may be downloadedover a network to persistent storage 1508 from another device or dataprocessing system through computer readable signal media 1526 for usewithin data processing system 1500. For instance, program code stored ina computer readable storage medium in a server data processing systemmay be downloaded over a network from the server to data processingsystem 1500. The data processing system providing program code 1518 maybe a server computer, a client computer, or some other device capable ofstoring and transmitting program code 1518.

The different components illustrated for data processing system 1500 arenot meant to provide architectural limitations to the manner in whichdifferent embodiments may be implemented. The different illustrativeembodiments may be implemented in a data processing system includingcomponents in addition to or in place of those illustrated for dataprocessing system 1500. Other components shown in FIG. 15 can be variedfrom the illustrative examples shown. The different embodiments may beimplemented using any hardware device or system capable of runningprogram code. As one example, the data processing system may includeorganic components integrated with inorganic components and/or may becomprised entirely of organic components excluding a human being. Forexample, a storage device may be comprised of an organic semiconductor.

In another illustrative example, processor unit 1504 may take the formof a hardware unit that has circuits that are manufactured or configuredfor a particular use. This type of hardware may perform operationswithout needing program code to be loaded into a memory from a storagedevice to be configured to perform the operations.

For example, when processor unit 1504 takes the form of a hardware unit,processor unit 1504 may be a circuit system, an application specificintegrated circuit (ASIC), a programmable logic device, or some othersuitable type of hardware configured to perform a number of operations.With a programmable logic device, the device is configured to performthe number of operations. The device may be reconfigured at a later timeor may be permanently configured to perform the number of operations.Examples of programmable logic devices include, for example, aprogrammable logic array, a field programmable logic array, a fieldprogrammable gate array, and other suitable hardware devices. With thistype of implementation, program code 1518 may be omitted because theprocesses for the different embodiments are implemented in a hardwareunit.

In still another illustrative example, processor unit 1504 may beimplemented using a combination of processors found in computers andhardware units. Processor unit 1504 may have a number of hardware unitsand a number of processors that are configured to run program code 1518.With this depicted example, some of the processes may be implemented inthe number of hardware units, while other processes may be implementedin the number of processors.

In another example, a bus system may be used to implement communicationsframework 1502 and may be comprised of one or more buses, such as asystem bus or an input/output bus. Of course, the bus system may beimplemented using any suitable type of architecture that provides for atransfer of data between different components or devices attached to thebus system.

Additionally, a communications unit may include a number of more devicesthat transmit data, receive data, or transmit and receive data. Acommunications unit may be, for example, a modem or a network adapter,two network adapters, or some combination thereof. Further, a memory maybe, for example, memory 1506, or a cache, such as found in an interfaceand memory controller hub that may be present in communicationsframework 1502.

The description of the different illustrative embodiments has beenpresented for purposes of illustration and description, and is notintended to be exhaustive or limited to the embodiments in the formdisclosed. Many modifications and variations will be apparent to thoseof ordinary skill in the art. Further, different illustrativeembodiments may provide different features as compared to otherillustrative embodiments. The embodiment or embodiments selected arechosen and described in order to best explain the principles of theembodiments, the practical application, and to enable others of ordinaryskill in the art to understand the disclosure for various embodimentswith various modifications as are suited to the particular usecontemplated.

1-16. (canceled)
 17. An apparatus comprising: a heuristic modelconfigured to generate estimated deformation data for a structure basedon input strain data; and a trainer configured to identify trainingdeformation data and training strain data for each training case in aplurality of training cases, train the heuristic model using thetraining deformation data and the training strain data identified forthe each training case in the plurality of training cases such that theheuristic model generates the estimated deformation data for thestructure based on the input strain data in which the estimateddeformation data has a desired level of accuracy, and receive thetraining strain data for the each training case in the plurality oftraining cases from a sensor system associated with the structure inwhich the sensor system comprises a plurality of sensors positioned at aplurality of points on the structure in which the plurality of sensorsis configured to generate a plurality of strain measurements for theplurality of points on the structure.
 18. (canceled)
 19. The apparatusof claim 17, wherein the heuristic model is configured to adjust a groupof parameters for the heuristic model using the training deformationdata and the training strain data for the each training case in theplurality of training cases such that the estimated deformation datagenerated by the heuristic model based on the input strain data has thedesired level of accuracy.
 20. The apparatus of claim 17, wherein theheuristic model comprises at least one of a neural network, alearning-based algorithm, a regression model, a support vector machine,a data fitting model, and a pattern recognition model.
 21. The apparatusof claim 17, wherein the structure is associated with a platform andwherein the estimated deformation data generated by the heuristic modelis used to adjust a group of control parameters for the structure duringoperation of the platform such that the structure has a desired level ofperformance during the operation of the platform.
 22. An antennadeformation modeling system comprising: a phased array antenna; a sensorsystem comprising a plurality of sensors embedded at a plurality ofpoints in the phased array antenna, the plurality of sensors configuredto generate strain data for the phased array antenna in a plurality oftraining cases, in which the plurality of sensors is configured togenerate a plurality of strain measurements for the plurality of pointson the phased array antenna; and a trainer configured to identifytraining deformation data and training strain data for each trainingcase in the plurality of training cases, train a heuristic model usingthe training deformation data and the training strain data identifiedfor the each training case in the plurality of training cases such thatthe heuristic model generates an estimated deformation data for thephased array antenna based on the input strain data, and receive thetraining strain data for the each training case in the plurality oftraining cases from the sensor system.
 23. The antenna deformationmodeling system of claim 22, further comprising an imaging systemconfigured to generate a number of images of the structure.
 24. Theantenna deformation modeling system of claim 23, wherein the imagingsystem comprises a number of cameras configured to generate the numberof images, the number of images including a number of deformationmeasurements at the plurality of points on the phased array antenna, thenumber of deformation measurements corresponding to the trainingdeformation data.
 25. The antenna deformation modeling system of claim22 further comprising an actuator system configured to apply a number ofloads on the phased array antenna for the plurality of training cases.26. The antenna deformation modeling system of claim 25, wherein theactuator system comprises a plurality of actuators configured to applythe number of loads to a plurality of points on the phased arrayantenna.
 27. The antenna deformation modeling system of claim 22,wherein the phased array antenna is associated with an aircraft anddeforms while the aircraft is in flight such that a shape of the phasedarray antenna changes from a reference shape to a deformed shape. 28.The antenna deformation modeling system of claim 22, wherein theestimated deformation data has a desired level of accuracy.
 29. A systemfor identifying deformation of a structure, comprising: a sensor systemassociated with the structure and comprising a plurality of sensorspositioned at a plurality of points on the structure generating straindata for the structure, in which the plurality of sensors is configuredto generate a plurality of strain measurements for the plurality ofpoints on the structure; an actuator system configured to apply a numberof loads on the structure for a number of training cases; an imagingsystem configured to generate a number of images of the structurereceiving the number of loads; and a computer system comprising atrainer configured to receive the strain data for each of the number oftraining cases from the sensor system and train a heuristic model usingthe strain data and the number of images.
 30. The system of claim 29,wherein the structure comprises a phased array antenna.
 31. The systemof claim 30, wherein the actuator system comprises a plurality ofactuators configured to apply the number of loads to a plurality ofpoints on the phased array antenna.
 32. The system of claim 31, whereinthe imaging system comprises a number of cameras configured to generatethe number of images, the number of images including a number ofdeformation measurements at the plurality of points on the phased arrayantenna.
 33. The system of claim 29, wherein the sensor system comprisesa plurality of strain gauges embedded in the structure.
 34. The systemof claim 29 further comprising a support system supporting thestructure.
 35. The system of claim 29, wherein the trainer is configuredto train the heuristic model to estimate a deformation of the structure.36. The system of claim 29, wherein the heuristic model comprises atleast one of a neural network, a learning-based algorithm, a regressionmodel, a support vector machine, a data fitting model, and a patternrecognition model.